Foundation Models Knowledge Distillation For Battery Capacity Degradation Forecast
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917176162648064 |
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| author | Chan, Joey Chen, Zhen Pan, Ershun |
| author_facet | Chan, Joey Chen, Zhen Pan, Ershun |
| contents | Accurate forecasting of lithium-ion battery capacity degradation is critical for reliable and safe operation, yet remains challenging under distribution shifts across scales and operating regimes. Here we investigate a time-series foundation model, that is, a large pre-trained time-series model for capacity degradation forecasting, and propose a degradation-aware fine-tuning strategy that aligns the model to capacity trajectories while retaining broadly transferable temporal structure. We instantiate this approach by fine-tuning the Timer model on 220,153 cycles of open-source charge-discharge records to obtain Battery-Timer. Using our released CycleLife-SJTUIE dataset, a real-world industrial collection from an energy-storage station with long-horizon cycling, we evaluate capacity generalization from small cells to large-scale storage systems and across varying operating conditions. Battery-Timer consistently outperforms specialized expert models. To address deployment cost, we further introduce knowledge distillation, a teacher-student transfer that compresses the foundation model's behavior into compact expert models. Distillation across several state-of-the-art time-series experts improves multi-condition capacity generalization while substantially reducing computational overhead, indicating a practical path to deployable cross-scale degradation forecasting by combining a foundation model with targeted distillation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08151 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Foundation Models Knowledge Distillation For Battery Capacity Degradation Forecast Chan, Joey Chen, Zhen Pan, Ershun Artificial Intelligence Accurate forecasting of lithium-ion battery capacity degradation is critical for reliable and safe operation, yet remains challenging under distribution shifts across scales and operating regimes. Here we investigate a time-series foundation model, that is, a large pre-trained time-series model for capacity degradation forecasting, and propose a degradation-aware fine-tuning strategy that aligns the model to capacity trajectories while retaining broadly transferable temporal structure. We instantiate this approach by fine-tuning the Timer model on 220,153 cycles of open-source charge-discharge records to obtain Battery-Timer. Using our released CycleLife-SJTUIE dataset, a real-world industrial collection from an energy-storage station with long-horizon cycling, we evaluate capacity generalization from small cells to large-scale storage systems and across varying operating conditions. Battery-Timer consistently outperforms specialized expert models. To address deployment cost, we further introduce knowledge distillation, a teacher-student transfer that compresses the foundation model's behavior into compact expert models. Distillation across several state-of-the-art time-series experts improves multi-condition capacity generalization while substantially reducing computational overhead, indicating a practical path to deployable cross-scale degradation forecasting by combining a foundation model with targeted distillation. |
| title | Foundation Models Knowledge Distillation For Battery Capacity Degradation Forecast |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.08151 |